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Toward privacy in IoT mobile devices for activity recognition

Théo Jourdan 1 Antoine Boutet 2 Carole Frindel 3 
2 PRIVATICS - Privacy Models, Architectures and Tools for the Information Society
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services, Inria Lyon
3 Images et Modèles
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
Abstract : Recent advances in wireless sensors for personal healthcare allow to recognise human real-time activities with mobile devices. While the analysis of those datastream can have many benefits from a health point of view, it can also lead to privacy threats by exposing highly sensitive information. In this paper, we propose a privacy-preserving framework for activity recognition. This framework relies on a machine learning technique to efficiently recognise the user activity pattern, useful for personal healthcare monitoring, while limiting the risk of re-identification of users from biometric patterns that characterizes each individual. To achieve that, we first deeply analysed different features extraction schemes in both temporal and frequency domain. We show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. On the basis of this observation, we second design a novel protection mechanism that processes the raw signal on the user's smartphone and transfers to the application server only the relevant features unlinked to the identity of users. In addition, a generalisation-based approach is also applied on features in frequency domain before to be transmitted to the server in order to limit the risk of re-identification. We extensively evaluate our framework with a reference dataset: results show an accurate activity recognition (87%) while limiting the re-identifation rate (33%). This represents a slightly decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines.
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Submitted on : Wednesday, September 26, 2018 - 7:08:49 PM
Last modification on : Tuesday, October 18, 2022 - 4:23:21 AM
Long-term archiving on: : Thursday, December 27, 2018 - 3:35:26 PM


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  • HAL Id : hal-01882330, version 1


Théo Jourdan, Antoine Boutet, Carole Frindel. Toward privacy in IoT mobile devices for activity recognition. MobiQuitous 2018 - 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Nov 2018, New York city, United States. pp.1-10. ⟨hal-01882330⟩



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